Classification methods for evaluating and analyzing absolute positioning accuracy

Classification and analysis of absolute positioning accuracy evaluation indicators

Abstract: With the development of positioning technology, absolute positioning accuracy evaluation indicators have become an important tool for evaluating the performance of positioning systems . This article will classify and analyze absolute positioning accuracy evaluation indicators, and give code examples in actual scenarios.

1. Introduction
   Positioning technology plays an important role in modern society, including Global Satellite Positioning System (GNSS), Bluetooth positioning, Wi-Fi positioning, and inertial navigation positioning. In order to evaluate the performance of positioning systems, absolute positioning accuracy evaluation indicators have become an indispensable tool. This article will classify and analyze absolute positioning accuracy evaluation indicators, and give code examples in actual scenarios.
2. Classification of absolute positioning accuracy evaluation indicators
   According to the characteristics of absolute positioning accuracy evaluation indicators, they can be divided into the following categories:

2.1 Positioning error related indicators
Positioning error related indicators mainly evaluate the deviation between the positioning result and the real position. Common positioning error-related indicators include Mean Position Error (MPE), Root Mean Square Error (RMSE), Maximum Position Error (MPE), etc. These indicators can intuitively reflect the accuracy of positioning results.

2.2 Computational complexity-related indicators
Computational complexity-related indicators mainly evaluate the computational efficiency of the positioning algorithm. Common computational complexity indicators include calculation time, storage space, and energy consumption. These indicators are particularly important for real-time positioning systems and can directly affect their stability and reliability.

2.3 Environmental adaptability related indicators
Environmental adaptability related indicators mainly evaluate the performance of the positioning system under different environmental conditions. Common environmental adaptability indicators include changes in positioning error under different environmental conditions, anti-interference ability, and multipath effect suppression. These indicators can help us choose a positioning system suitable for different scenarios.

3. Analysis of absolute positioning accuracy evaluation indicators
   In practical applications, absolute positioning accuracy evaluation indicators need to be analyzed according to specific scenarios and needs. The following is a code example based on GNSS positioning system:

# 导入必要的模块
import numpy as np

# 真实位置
true_position = np.array([30.0, 120.0])

# 定位结果
estimated_position = np.array([30.5, 121.0])

# 计算平均定位误差
mpe = np.mean(np.abs(estimated_position - true_position))
print("平均定位误差：", mpe)

# 计算均方根误差
rmse = np.sqrt(np.mean(np.square(estimated_position - true_position)))
print("均方根误差：", rmse)

# 计算最大定位误差
mpe = np.max(np.abs(estimated_position - true_position))
print("最大定位误差：", mpe)

In the above code, we first give the real position and positioning results, and calculate the average positioning error and root mean square through relevant formulas error and the maximum positioning error. These indicators directly reflect the precision and accuracy of the positioning system.

4. Conclusion
   This article classifies and analyzes the absolute positioning accuracy evaluation indicators, and provides code examples based on the GNSS positioning system. The absolute positioning accuracy evaluation index is of great significance for evaluating the performance of the positioning system and can help us select and optimize appropriate positioning technology. At the same time, in practical applications, we need to analyze indicators according to specific scenarios and needs to obtain more accurate evaluation results.

References:
[1] Zhang, K., Sui, Q., & Bi, Y. (2017). A Review on Localization Strategies for Wireless Sensor Networks. Sensors (Basel , Switzerland), 17(6), 1303.
[2] LaMarca, A., & Chawathe, Y. (2005). Location Systems: An Introduction to the Technology Behind GPS. Synthesis Lectures on Mobile and Pervasive Computing, 1(1), 1-56.
[3] Li, C., Luo, Y., Wang, Z. J., Zhang, P., & Song, H. (2019). A Survey on Advanced Localization Techniques for 5G/B5G Wireless Networks. IEEE Communications Surveys and Tutorials, 21(1), 256-281.

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